Comparison of the Performance of ClimGen and LARS-WG Models in Simulating the Weather Factors for Diverse Climates of Iran

چکیده [English]

The stochastic simulation models of weather factors (weather generators) are used in a wide range of studies including risk assessment of climatic and hydrological extreme events, water resources, and agricultural risk management. Such studies often need access to the long-term series of weather data which is not collected continuously in many meteorological stations in Iran. Due to this shortage of data (particularly, the daily data), stochastic weather generators can be used as an alternative for extending data series. These generators are used to produce the synthetic weather data which is statistically similar to the observed data. In this study, the two well-known weather generators, i.e., ClimGen and LARS-WG were evaluated in simulating the weather factors for fifteen climatic zones of the country. The weather factors include the daily total precipitation, the minimum and maximum air temperatures, and the total solar radiation. For this purpose, the process of generating synthetic weather data was divided into three distinct steps including model calibration, model validation, and long-term simulation of weather data. To evaluate the agreement between the observed and the generated data, two indices were used; Root Mean Square Error (RMSE) and Coefficient of Determination (CD). Moreover, the three statistical tests including t-student test, F test and X2 test were used to compare the various characteristics of the simulated and observed data such as the lengths of wet and dry series, the distribution of precipitation, and the lengths of hot and frost spells. The results showed that LARS-WG tends to match more closely to the observed precipitation data. The ClimGen performed better for the observed maximum and minimum air temperatures. Neither of the two weather generators succeeded in simulating total solar radiation